Huffman coding-based data reduction and quadristego logic for secure image steganography

Ensuring secure data transmission is critical for maintaining confidentiality and has become increasingly important. Steganography, which embeds secret information within digital images, has been widely explored to safeguard sensitive data from unauthorized access over public networks. However, existing steganographic algorithms often face a significant trade-off between payload capacity, image quality, and security. Embedding large amounts of data can cause noticeable distortion in image quality, undermining the technique’s effectiveness. Furthermore, current methods lack adaptability to diverse cover media and struggle to maintain reversibility and high visual quality under increased embedding capacities. To address these challenges, this study proposes a novel steganographic algorithm integrating two key innovations: (1) Enhancement of stego image quality via stego image’s segmentation into four images, reducing concentration-induced distortions, and (2) optimization of data embedding through Huffman coding through a lossless compression minimizing the embedding-induced distortions while maximizing payload capacity. The experimental results show that the proposed method achieves high visual fidelity, with PSNR values ranging from 75.793 dB to 44.997 dB without encryption and from 51.159 dB to 44.316 dB with encryption for payloads between 10 KB and 100 KB. These values exceed the 30 dB threshold for acceptable image steganography, ensuring minimal perceptual distortion. Additionally, the SSIM remains consistently above 0.98, indicating strong structural preservation of stego images. Comparative analysis with existing methods confirms that the proposed approach outperforms in embedding capacity, structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR), reflecting the stego images’ quality.